Responses by Pedro Barroso, senior designer; Laura Cortes, creative director; Mel Eshaghbeigi, strategist; Sukh Gill, technical director; Alexandra Hook, art director; Neo Li, lead developer; and Leslie Uy, associate executive producer, Jam3.

Background: “The target audience of Yesterday, Today, Tomorrow is the pandemic’s target audience,” says Mel Eshaghbeigi. “COVID-19 has oddly become somewhat of a unifier even though not everyone is experiencing the same challenges—some of us have lost our jobs or families, others have gained new skills and new perspectives. We are all experiencing something in response to this pandemic. We wanted to create a space to look back at and look deeper into all these stories, all these moments as they happened, how we reacted to them, how we felt, and how we went from fear and uncertainty to what’s been branded as a ‘new normal.’ This is our collective diary, our digital archive and our way to make sense of what will be a life-defining chapter of all of our lives.”

Favorite details: “This was the first time Jam3 was commissioned as an offer,” says Laura Cortes. “It wasn’t our usual client brief but a true collaboration, where the National Film Board of Canada (NFB) wanted to honor the talents and creative minds involved. Now, six months into the pandemic while we’re still going through a very unique and distressing time in our lives, I am truly proud of what we’ve accomplished: a beautiful portrayal of what we all were and are personally living. A picture of the world as much as a reflection of the team at Jam3 that laughed and sometimes cried together. I can say we proved that we are just not just executioners: we are thinkers and we are artists. It’s possible to build a truly collaborative approach, no matter the circumstances.”

Challenges: “Creating the site remotely was the first and largest challenge,” Alexandra Hook recalls. “We crossed four time zones and four countries. The normal ease of leaning over to your colleague to make a quick comment was lost. Essentially, we had to develop new processes to perform normally basic tasks. The next largest challenge was the data, beginning to work with a large dataset takes time and negotiation. So we had to design a system that would represent the data fairly and be flexible enough to adapt before we ever got our hands on the actual tweets. I feel like we did a pretty great job in the end, but this was a testament to a passionate team and tons of iterations.”

Time constraints: “Time went from being a constraint to a construct,” says Eshaghbeigi. “Our days became fluid, blending into each other seamlessly, floating from meeting to meeting, coming across challenges and obstacles at each turn. Twitter didn’t want to make us an official data partner? No problem. You’re working across four timezones? No worries! We moved through this project like we did the pandemic—with hope, passion, perseverance and togetherness.”

New lessons: “What we learned during this process is how quickly time has passed, how much has happened, and how so much has changed and so much hasn’t,” says Leslie Uy. “We learned how resilient people are during a pandemic and how adaptable we can be to normalize the situation (i.e., wearing masks.) It was a bit alarming to look back at all those moments—all those major news stories, all the things that happened that threw us into a state of fear and concern—to look back on this year, realize that it’s September and that we started this project in March.”

Navigation structure: “We always perceived this experience as a story,” says Pedro Barroso, “a narrative with a beginning, a middle and—much like the pandemic we face—hopefully an end. The use of the circle as our primary navigation visually translates this concept while simultaneously supporting the variety of data we had, represented by the bubbles. In this context, time creates a cyclical flow for conversation, giving an organic structure to the data.”

Technology: “The technical architecture is a combination of AWS services, integrated with IBM Watson and Twitter API endpoints all behind a server less framework leveraged in a React application,” says Neo Li.

“The data layer that controls the gathering and processing of Twitter data and IBM Watson to determine sentiments around COVID-19 timing and reactions to key global events,” says Sukh Gill. “The data was leveraged to create a timeline of feelings grouped and ranked by categories.”